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Optimization design on the riser system of next generation subsea production system with the assistance of DOE and surrogate model techniques
Institution:1. Solom-Aleichem Priamursky State University, Birobidzhan 679015, Russian Federation;2. Institute of Machine Science and Metallurgy, Far Eastern Branch Russian Academy of Sciences, Komsomolsk-na-Amure 681005, Russian Federation;3. Komsomolsk-na-Amure State University, Komsomolsk-na-Amure 681013, Russian Federation;1. Department of Civil Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece;2. Department of Naval Architecture and Ocean Engineering, Osaka University, Suita, Osaka, 5650871, Japan
Abstract:The Next Generation Subsea Production System (NextGen SPS) is a new concept for petroleum development in ultra-deep water (UDW) areas. It can improve the structural performance of riser as well as provide several operational benefits to subsurface well completion (SWC) equipment. The design of NextGen SPS’s riser system which includes rigid riser and flexible jumper—like the free standing hybrid riser (FSHR), is a very important issue for the definition of NextGen SPS. This paper details an optimization design on the NextGen SPS’s riser system, with the assistance of the design of experiments (DOE) and surrogate model techniques. The optimization model pertaining to riser system is formulated firstly. The DOE is a statistical technique that guides a sensitive study on the behavior of the riser system before the optimization analysis. Structural responses are obtained by the fully coupled methodology. Through such a preliminary study, the effective contribution of each design variable at the riser performance will be known and some general conclusive remarks will be obtained. Based on the DOE results, design variables are screened to improve the efficiency of optimization process. Particle swarm optimization (PSO) method is employed to conduct the optimization analysis. In this analysis, surrogate models, which are developed by back propagation neural network (BPNN), replace the time consuming dynamic analysis to predict structural responses. Latin hypercube sampling (LHS) method is adopted to generate training sample and testing sample for the BPNN. NextGen SPS that operates at a depth of 3000 m is used as the case for this investigation. The efficiency of optimization design is improved by DOE and surrogate techniques, and a reduction of approximately 46% for the riser system cost is achieved. The obtained conclusions have applicability in reference to the engineering design of FSHR and the study procedure will provide reference for study on other new structure concept.
Keywords:Next generation subsea production system  Riser system  Optimization design  Design of experiments  Surrogate model
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